Beamforming Training With CSI Mapping for Optimal MIMO Channels
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Solution Overview
Problem
Current millimeter-wave communication systems face challenges in accurately determining optimal antenna configurations and channel configurations due to insufficient feedback of channel state information (CSI) and modulation and coding scheme (MCS), leading to suboptimal beamforming training and reduced communication rates, especially in scenarios requiring channel aggregation and orthogonal polarization.
Innovation Solution
A beamforming training method that maps transmit antennas, sectors, and channels to receive chain SNR, MCS, and CSI, providing feedback on optimal MIMO configurations and indicating the need for cyclic shift diversity (CSD) based on measurement results to enhance packet detection and decoding success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If channel aggregation and orthogonal polarization are used to increase data rate, then communication rate increases to more than 20 gigabits per second, but accuracy of channel capacity learning from feedback results is not high and optimal antenna configuration cannot be determined
Solution Approach 1:
The patent segments the feedback process by separately feeding back CSI for each spatial stream and channel combination, rather than providing aggregated feedback. This allows the receiving device to learn channel capacity accuracy for each individual stream-channel pair, enabling precise determination of optimal antenna configurations in channel aggregation scenarios.
Solution Approach 2:
The patent enhances the feedback mechanism by including detailed CSI feedback for each spatial stream and channel combination, along with recommendations for cyclic shift diversity (CSD) values. This comprehensive feedback enables the transmitting device to accurately learn channel characteristics and determine optimal antenna and channel configurations.
2Productivity
If MIMO technology is used with multiple antennas and channels to increase data rate, then communication capacity increases, but device complexity increases due to need for beamforming training and channel state information feedback
Solution Approach 1:
The patent performs beamforming training and channel state estimation as preliminary actions before actual data transmission. By completing the beamforming training protocol and obtaining CSI feedback in advance, the system prepares optimal antenna configurations and CSD values beforehand, simplifying the actual data transmission process.
Solution Approach 2:
The patent changes system parameters by determining optimal CSD values based on channel conditions and feedback results. By adjusting CSD parameters according to learned channel capacity accuracy, the system optimizes MIMO performance while managing complexity through adaptive parameter selection.
3Reliability
If cyclic shift diversity is applied to all spatial streams, then packet detection rate and decoding success rate improve, but communication efficiency decreases due to unnecessary CSD application
Solution Approach 1:
The patent applies CSD selectively to individual spatial streams based on their specific channel conditions and correlation characteristics, rather than uniformly applying CSD to all streams. This localized approach ensures CSD is applied only where necessary to improve packet detection, maintaining communication efficiency for streams that do not require it.
Solution Approach 2:
The patent dynamically changes CSD parameters by determining optimal CSD values for each spatial stream based on channel state information and feedback results. This adaptive parameter adjustment allows the system to apply CSD only when channel conditions warrant it, balancing reliability improvement with communication efficiency.
Data Source
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AI summary
This application provides a beamforming training method, a receiving device, and a sending device. The method includes: performing BF training on at least one channel with a first device based on BF training request information, where the BF training request information includes antenna configuration information of the BF training and channel configuration information of the at least one channel; receiving first feedback information sent by the first device, where the first feedback information includes a measurement result of the BF training, information about an antenna corresponding to the measurement result, beam information of the antenna, and channel information corresponding to the antenna; and determining an optimal antenna configuration and/or digital domain BF precoding information on the at least one channel based on the first feedback information. According to the beamforming training method provided in this application, a transmit antenna, a transmit sector, and a channel in a BF training feedback are mapped to an SNR/an MCS/CSI of a receive chain, thereby learning of a maximum channel capacity, and obtaining an optimal MIMO channel configuration.